Image Segmentation Research Based on Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 1622-1625
Author(s):  
Xiao Wei Guan ◽  
Xia Zhu
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Feature Representation ◽  
The Other ◽  
Ga Algorithm ◽  
Representation Method ◽  
Optimal Feature ◽  
Image Segmentation Algorithm

As one of the difficulties and hot of computer vision and image processing, Image segmentation is highly valued by the research workers. Yet there is no image segmentation algorithm which is generic, and it is difficult to obtain an optimal feature representation method. In this paper, genetic algorithm (GA) has proposed to segment the image. GA algorithm can improve the efficiency and quality of the picture some extent through the experimental results. The algorithm has some versatility, as long as the corresponding parameters are adjusted, it can also handle the other images. The results show that GA algorithm is very stable, and the fusion result is more satisfactory. Thus, GA can be applied in image segmentation and this algorithm will have good prospects in image processing.

Image Segmentation Research Based on Particle Swarm Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 1644-1647
Author(s):  
Xia Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feature Representation ◽  
Swarm Optimization ◽  
Representation Method ◽  
Optimal Feature ◽  
Image Segmentation Algorithm

As one of the difficulties and hot of computer vision and image processing, Image segmentation is highly valued by the research workers. Yet there is no image segmentation algorithm which is generic, and it is difficult to obtain an optimal feature representation method. In this paper, particle swarm optimization has proposed to segment the image. PSO algorithm can improve the efficiency and quality of the picture some extent through the experimental results. The algorithm has some versatility, as long as the corresponding parameters are adjusted, it can also handle the other images. The results show that PSO algorithm is very stable, and the fusion result is more satisfactory.

Research on the Automatic Multilevel Thresholding for Athletic Image Segmentation Based on Block Sampling and Genetic Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 2007-2012
Author(s):  
Xiao Guang Li
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Meaningful Work ◽  
Image Processing Algorithm ◽  
Computer Image Processing ◽  
Ga Algorithm ◽  
Block Sampling ◽  
The Cost ◽  
Image Segmentation Algorithm

The research on new image segmentation algorithm is a very meaningful work in the processing of image. In the process, it will produce large amount of data redundancy. The efficient algorithm not only can greatly improve the quality of image treatment but also can greatly reduce the time and cost of the treatment. In this context, the paper analyzes several image processing algorithms commonly used in recent years and presents a new computer image processing algorithm--AMT-GA algorithm. In order to verify the effectiveness of AMT-GA algorithm, this paper takes the process of athletics image for example and compares the consistency and time of image segmentation with other literature results and ultimately finds that the consistency of AMT-GA algorithm reaches 0.99. The time in the algorithm execution is only 0.81 which not only achieves effective segmentation of the image but also saves the cost of computing. It also provides a theoretical reference for the research of computer graphics technology.

Research and Implementation of a Fast Image Segmentation Algorithm Based on SRG

2014 ◽  
Vol 621 ◽  
pp. 594-598
Author(s):  
Chun Yin Hu ◽  
Wan Cheng Tang ◽  
Bang Yan Ye ◽  
Li Dong Liang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Virus Infection ◽  
Region Growing ◽  
The Other ◽  
Image Region ◽  
Image Scanning ◽  
Time Performance ◽  
Seeded Region Growing ◽  
Image Segmentation Algorithm

In order to improve the real-time performance and accuracy of the traditional SRG(Seeded Region Growing) algorithm in image processing, this paper proposes a intellective and rapid image segmentation by imitating the process of the virus infection in nature, and then implement it on vc++6 platform. On one hand , the algorithm can detecting automatically detect the seeds in image region and can be adapt for uneven-light image by adjusting the parameters based on the brightness of the background; On the other hand, only by one of the image scanning, it can segment and mark the objects from the background. The experimental results show that compared with the traditional SRG algorithm, this algorithm can improve the segmentation speed in different background with higher accuracy.

Image Segmentation Research Based on GA and Improved Otsu Algorithm

2017 ◽  
Vol 7 (2) ◽  
pp. 533
Author(s):  
Xiaoqun Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Two Dimensional ◽  
One Dimensional ◽  
Otsu Method ◽  
Segmented Object ◽  
The Face ◽  
Image Segmentation Algorithm ◽  
Small Dispersion ◽  
Better Than

<p>In the face of the problem of high complexity of two-dimensional Otsu adaptive threshold algorithm, a new fast and effective Otsu image segmentation algorithm is proposed based on genetic algorithm. This algorithm replaces the segmentation threshold of the traditional two - dimensional Otsu method by finding the threshold of two one-dimensional Otsu method, it reduces the computational complexity of the partition from O (L4) to O (L). In order to ensure the integrity of the segmented object, the algorithm introduces the concept of small dispersion in class, and the automatic optimization of parameters are achieved by genetic algorithm. Theoretical analysis and experimental results show that the algorithm is not only better than the original two-dimensional Otsu algorithm, but also it has better segmentation effect.</p>

scholarly journals ANN analysis in a vision approach for potato inspection

2008 ◽  
Vol 6 (02) ◽  
Author(s):  
R. Rios-Cabrera ◽  
I Lopez-Juarez ◽  
Hsieh Sheng-Jen
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Comparative Analysis ◽  
Physical Properties ◽  
Real Time ◽  
The Other ◽  
Stability And Convergence

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

scholarly journals Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuxin Guo ◽  
Liping Hou ◽  
Wen Zhu ◽  
Peng Wang
Keyword(s):  
Prediction Model ◽  
Naive Bayes ◽  
Experimental Period ◽  
Naïve Bayes ◽  
Feature Representation ◽  
Hormone Binding ◽  
Hormone Binding Protein ◽  
Representation Method ◽  
Optimal Feature

Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body’s life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.

scholarly journals An Extensive Review on Image Segmentation Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
Kamlesh Sharma ◽  
Nidhi Garg
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Digital Images ◽  
Extensive Review ◽  
Current Image ◽  
Segmentation Image

Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 925-928 ◽  
Author(s):  
Zhi Bo Xu ◽  
Pei Jiang Chen ◽  
Shi Li Yan ◽  
Tai Hua Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Image Process ◽  
Segmentation Method ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Maximum Variance ◽  
Segmentation Image ◽  
Selection Of

Threshold segmentation method was widely applied in image process and the selection of threshold affected the final results of image segmentation to a large extent. In order to improve the accuracy and the calculation speed of image segmentation, an Otsu threshold segmentation method based on genetic algorithm was offered. According to the threshold and the gray scale values of pixels, the pixels were divided into two categories, and then the genetic algorithm was used to find the maximum variance between clusters and obtain the optimal threshold of segmentation image. The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step.

Fuzzy Clustering Color Image Segmentation Algorithm Based on CPSO

2012 ◽  
Vol 461 ◽  
pp. 526-531
Author(s):  
Xiao Hong Zhang ◽  
Hong Mei Ning
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Fcm Clustering ◽  
Clustering Center ◽  
Membership Matrix ◽  
Image Segmentation Algorithm

Fuzzy C-mean algorithm (FCM) has been well used in the field of color image segmentation. But it is sensitive to initial clustering center and membership matrix, and likely converges into the local minimum, which causes the quality of image segmentation lower. By use of the properties-ergodicity, randomicity of chaos, a new image segmentation algorithm is proposed, which combines the chaos particle swarm optimization (CPSO) and FCM clustering. Some experimental results are shown that this method not only has the ability to prevent the particles to convergence to local optimum, but also has faster convergence and higher accuracy for segmentation. Using the feature distance instead of Euclidian distance, robustness of this method is enhanced.

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